Customer relationship report

Dataset statistics

Number of variables	25
Number of observations	8343
Missing cells	0
Missing cells (%)	0.0%
Duplicate rows	0
Duplicate rows (%)	0.0%
Total size in memory	1.6 MiB
Average record size in memory	200.0 B

Variable types

Numeric	12
Categorical	13

Warnings

`f2f_total_12` has constant value "0"	Constant
`webinar_total_12` has constant value "0"	Constant
`f2f_total_03` is highly correlated with `f2f_total_06` and 1 other fields	High correlation
`f2f_total_06` is highly correlated with `f2f_total_03` and 1 other fields	High correlation
`f2f_total_09` is highly correlated with `f2f_total_03` and 1 other fields	High correlation
`is_cardiologist` is highly correlated with `is_gp`	High correlation
`is_gp` is highly correlated with `is_cardiologist`	High correlation
`gender_female` is highly correlated with `gender_male`	High correlation
`gender_male` is highly correlated with `gender_female`	High correlation
`hospital` is highly correlated with `office`	High correlation
`office` is highly correlated with `hospital`	High correlation
`f2f_total_03` is highly correlated with `f2f_total_06` and 1 other fields	High correlation
`f2f_total_06` is highly correlated with `f2f_total_03` and 1 other fields	High correlation
`f2f_total_09` is highly correlated with `f2f_total_03` and 1 other fields	High correlation
`is_cardiologist` is highly correlated with `is_gp`	High correlation
`is_gp` is highly correlated with `is_cardiologist`	High correlation
`gender_female` is highly correlated with `gender_male`	High correlation
`gender_male` is highly correlated with `gender_female`	High correlation
`hospital` is highly correlated with `office`	High correlation
`office` is highly correlated with `hospital`	High correlation
`f2f_total_06` is highly correlated with `f2f_total_09`	High correlation
`f2f_total_09` is highly correlated with `f2f_total_06`	High correlation
`is_cardiologist` is highly correlated with `is_gp`	High correlation
`is_gp` is highly correlated with `is_cardiologist`	High correlation
`gender_female` is highly correlated with `gender_male`	High correlation
`gender_male` is highly correlated with `gender_female`	High correlation
`hospital` is highly correlated with `office`	High correlation
`office` is highly correlated with `hospital`	High correlation
`hospital` is highly correlated with `office` and 2 other fields	High correlation
`gender_male` is highly correlated with `gender_female`	High correlation
`rep_id` is highly correlated with `is_cardiologist` and 1 other fields	High correlation
`office` is highly correlated with `hospital` and 2 other fields	High correlation
`f2f_total_03` is highly correlated with `is_cardiologist` and 3 other fields	High correlation
`is_cardiologist` is highly correlated with `hospital` and 4 other fields	High correlation
`f2f_total_06` is highly correlated with `f2f_total_03` and 1 other fields	High correlation
`gender_female` is highly correlated with `gender_male`	High correlation
`webinar_total_03` is highly correlated with `webinar_total_06`	High correlation
`webinar_total_06` is highly correlated with `webinar_total_03`	High correlation
`is_gp` is highly correlated with `hospital` and 4 other fields	High correlation
`f2f_total_09` is highly correlated with `f2f_total_03` and 1 other fields	High correlation
`conference_total_03` is highly correlated with `webinar_total_12` and 1 other fields	High correlation
`is_cardiologist` is highly correlated with `is_gp` and 2 other fields	High correlation
`office` is highly correlated with `webinar_total_12` and 2 other fields	High correlation
`is_gp` is highly correlated with `is_cardiologist` and 2 other fields	High correlation
`gender_female` is highly correlated with `webinar_total_12` and 2 other fields	High correlation
`webinar_total_12` is highly correlated with `conference_total_03` and 11 other fields	High correlation
`conference_total_09` is highly correlated with `webinar_total_12` and 1 other fields	High correlation
`hospital` is highly correlated with `office` and 2 other fields	High correlation
`conference_total_06` is highly correlated with `webinar_total_12` and 1 other fields	High correlation
`gender_male` is highly correlated with `gender_female` and 2 other fields	High correlation
`conference_total_12` is highly correlated with `webinar_total_12` and 1 other fields	High correlation
`webinar_total_09` is highly correlated with `webinar_total_12` and 1 other fields	High correlation
`f2f_total_12` is highly correlated with `conference_total_03` and 11 other fields	High correlation
`email_open_total_03` has 3095 (37.1%) zeros	Zeros
`email_open_total_06` has 3026 (36.3%) zeros	Zeros
`email_open_total_09` has 3124 (37.4%) zeros	Zeros
`email_open_total_12` has 3041 (36.4%) zeros	Zeros
`f2f_total_03` has 2152 (25.8%) zeros	Zeros
`f2f_total_06` has 2147 (25.7%) zeros	Zeros
`f2f_total_09` has 2486 (29.8%) zeros	Zeros
`webinar_total_03` has 7586 (90.9%) zeros	Zeros
`webinar_total_06` has 7596 (91.0%) zeros	Zeros
`prescription_total` has 1527 (18.3%) zeros	Zeros

Reproduction

Analysis started	2021-06-01 14:56:51.758083
Analysis finished	2021-06-01 14:57:10.023034
Duration	18.26 seconds
Software version	pandas-profiling v3.0.0
Download configuration	config.json

rep_id
Real number (ℝ_≥0)

HIGH CORRELATION

Distinct	153
Distinct (%)	1.8%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	154.2720844

Minimum	100
Maximum	252
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	65.3 KiB

Quantile statistics

Minimum	100
5-th percentile	102
Q1	124
median	149
Q3	185
95-th percentile	218
Maximum	252
Range	152
Interquartile range (IQR)	61

Descriptive statistics

Standard deviation	36.61065761
Coefficient of variation (CV)	0.2373122639
Kurtosis	-0.8445532806
Mean	154.2720844
Median Absolute Deviation (MAD)	29
Skewness	0.3773892915
Sum	1287092
Variance	1340.34025
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
102	360	4.3%
145	201	2.4%
191	175	2.1%
147	175	2.1%
125	172	2.1%
129	143	1.7%
151	135	1.6%
124	132	1.6%
153	126	1.5%
105	126	1.5%
Other values (143)	6598	79.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
100	91	1.1%
101	79	0.9%
102	360	4.3%
103	33	0.4%
104	31	0.4%
105	126	1.5%
106	123	1.5%
107	119	1.4%
108	55	0.7%
109	29	0.3%

Value	Count	Frequency (%)
252	1	< 0.1%
251	1	< 0.1%
250	1	< 0.1%
249	1	< 0.1%
248	1	< 0.1%
247	3	< 0.1%
246	1	< 0.1%
245	1	< 0.1%
244	14	0.2%
243	1	< 0.1%

conference_total_03
Categorical

HIGH CORRELATION

Distinct	3
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	65.3 KiB

0	7568
1	737
2	38

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	8343
Distinct characters	3
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	7568	90.7%
1	737	8.8%
2	38	0.5%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	7568	90.7%
1	737	8.8%
2	38	0.5%

Most occurring characters

Value	Count	Frequency (%)
0	7568	90.7%
1	737	8.8%
2	38	0.5%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	8343	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	7568	90.7%
1	737	8.8%
2	38	0.5%

Most occurring scripts

Value	Count	Frequency (%)
Common	8343	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
0	7568	90.7%
1	737	8.8%
2	38	0.5%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	8343	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	7568	90.7%
1	737	8.8%
2	38	0.5%

conference_total_06
Categorical

HIGH CORRELATION

Distinct	3
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	65.3 KiB

0	7521
1	779
2	43

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	8343
Distinct characters	3
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	7521	90.1%
1	779	9.3%
2	43	0.5%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	7521	90.1%
1	779	9.3%
2	43	0.5%

Most occurring characters

Value	Count	Frequency (%)
0	7521	90.1%
1	779	9.3%
2	43	0.5%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	8343	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	7521	90.1%
1	779	9.3%
2	43	0.5%

Most occurring scripts

Value	Count	Frequency (%)
Common	8343	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
0	7521	90.1%
1	779	9.3%
2	43	0.5%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	8343	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	7521	90.1%
1	779	9.3%
2	43	0.5%

conference_total_09
Categorical

HIGH CORRELATION

Distinct	4
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	65.3 KiB

0	7551
1	763
2	27
3	2

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	8343
Distinct characters	4
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	1
2nd row	1
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	7551	90.5%
1	763	9.1%
2	27	0.3%
3	2	< 0.1%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	7551	90.5%
1	763	9.1%
2	27	0.3%
3	2	< 0.1%

Most occurring characters

Value	Count	Frequency (%)
0	7551	90.5%
1	763	9.1%
2	27	0.3%
3	2	< 0.1%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	8343	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	7551	90.5%
1	763	9.1%
2	27	0.3%
3	2	< 0.1%

Most occurring scripts

Value	Count	Frequency (%)
Common	8343	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
0	7551	90.5%
1	763	9.1%
2	27	0.3%
3	2	< 0.1%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	8343	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	7551	90.5%
1	763	9.1%
2	27	0.3%
3	2	< 0.1%

conference_total_12
Categorical

HIGH CORRELATION

Distinct	4
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	65.3 KiB

0	7533
1	779
2	30
3	1

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	8343
Distinct characters	4
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1 ?
Unique (%)	< 0.1%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	0
5th row	1

Common Values

Value	Count	Frequency (%)
0	7533	90.3%
1	779	9.3%
2	30	0.4%
3	1	< 0.1%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	7533	90.3%
1	779	9.3%
2	30	0.4%
3	1	< 0.1%

Most occurring characters

Value	Count	Frequency (%)
0	7533	90.3%
1	779	9.3%
2	30	0.4%
3	1	< 0.1%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	8343	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	7533	90.3%
1	779	9.3%
2	30	0.4%
3	1	< 0.1%

Most occurring scripts

Value	Count	Frequency (%)
Common	8343	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
0	7533	90.3%
1	779	9.3%
2	30	0.4%
3	1	< 0.1%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	8343	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	7533	90.3%
1	779	9.3%
2	30	0.4%
3	1	< 0.1%

email_open_total_03
Real number (ℝ_≥0)

ZEROS

Distinct	8
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	0.9858564066

Minimum	0
Maximum	7
Zeros	3095
Zeros (%)	37.1%
Negative	0
Negative (%)	0.0%
Memory size	65.3 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	1
Q3	2
95-th percentile	3
Maximum	7
Range	7
Interquartile range (IQR)	2

Descriptive statistics

Standard deviation	0.9911095483
Coefficient of variation (CV)	1.005328506
Kurtosis	1.031497074
Mean	0.9858564066
Median Absolute Deviation (MAD)	1
Skewness	1.014062707
Sum	8225
Variance	0.9822981367
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=8)

Value	Count	Frequency (%)
1	3107	37.2%
0	3095	37.1%
2	1490	17.9%
3	503	6.0%
4	114	1.4%
5	32	0.4%
6	1	< 0.1%
7	1	< 0.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	3095	37.1%
1	3107	37.2%
2	1490	17.9%
3	503	6.0%
4	114	1.4%
5	32	0.4%
6	1	< 0.1%
7	1	< 0.1%

Value	Count	Frequency (%)
7	1	< 0.1%
6	1	< 0.1%
5	32	0.4%
4	114	1.4%
3	503	6.0%
2	1490	17.9%
1	3107	37.2%
0	3095	37.1%

email_open_total_06
Real number (ℝ_≥0)

ZEROS

Distinct	7
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	1.022414

Minimum	0
Maximum	6
Zeros	3026
Zeros (%)	36.3%
Negative	0
Negative (%)	0.0%
Memory size	65.3 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	1
Q3	2
95-th percentile	3
Maximum	6
Range	6
Interquartile range (IQR)	2

Descriptive statistics

Standard deviation	1.01137196
Coefficient of variation (CV)	0.9892000308
Kurtosis	0.6611379464
Mean	1.022414
Median Absolute Deviation (MAD)	1
Skewness	0.9391927051
Sum	8530
Variance	1.022873242
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=7)

Value	Count	Frequency (%)
0	3026	36.3%
1	3019	36.2%
2	1586	19.0%
3	539	6.5%
4	144	1.7%
5	28	0.3%
6	1	< 0.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	3026	36.3%
1	3019	36.2%
2	1586	19.0%
3	539	6.5%
4	144	1.7%
5	28	0.3%
6	1	< 0.1%

Value	Count	Frequency (%)
6	1	< 0.1%
5	28	0.3%
4	144	1.7%
3	539	6.5%
2	1586	19.0%
1	3019	36.2%
0	3026	36.3%

email_open_total_09
Real number (ℝ_≥0)

ZEROS

Distinct	8
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	0.9880139039

Minimum	0
Maximum	7
Zeros	3124
Zeros (%)	37.4%
Negative	0
Negative (%)	0.0%
Memory size	65.3 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	1
Q3	2
95-th percentile	3
Maximum	7
Range	7
Interquartile range (IQR)	2

Descriptive statistics

Standard deviation	0.9972872213
Coefficient of variation (CV)	1.009385817
Kurtosis	0.9381677242
Mean	0.9880139039
Median Absolute Deviation (MAD)	1
Skewness	0.9980709947
Sum	8243
Variance	0.9945818018
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=8)

Value	Count	Frequency (%)
0	3124	37.4%
1	3046	36.5%
2	1509	18.1%
3	508	6.1%
4	130	1.6%
5	22	0.3%
6	3	< 0.1%
7	1	< 0.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	3124	37.4%
1	3046	36.5%
2	1509	18.1%
3	508	6.1%
4	130	1.6%
5	22	0.3%
6	3	< 0.1%
7	1	< 0.1%

Value	Count	Frequency (%)
7	1	< 0.1%
6	3	< 0.1%
5	22	0.3%
4	130	1.6%
3	508	6.1%
2	1509	18.1%
1	3046	36.5%
0	3124	37.4%

email_open_total_12
Real number (ℝ_≥0)

ZEROS

Distinct	7
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	1.005633465

Minimum	0
Maximum	6
Zeros	3041
Zeros (%)	36.4%
Negative	0
Negative (%)	0.0%
Memory size	65.3 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	1
Q3	2
95-th percentile	3
Maximum	6
Range	6
Interquartile range (IQR)	2

Descriptive statistics

Standard deviation	0.9990846462
Coefficient of variation (CV)	0.9934878669
Kurtosis	0.8401131661
Mean	1.005633465
Median Absolute Deviation (MAD)	1
Skewness	0.9727254529
Sum	8390
Variance	0.9981701303
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=7)

Value	Count	Frequency (%)
1	3087	37.0%
0	3041	36.4%
2	1533	18.4%
3	522	6.3%
4	133	1.6%
5	23	0.3%
6	4	< 0.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	3041	36.4%
1	3087	37.0%
2	1533	18.4%
3	522	6.3%
4	133	1.6%
5	23	0.3%
6	4	< 0.1%

Value	Count	Frequency (%)
6	4	< 0.1%
5	23	0.3%
4	133	1.6%
3	522	6.3%
2	1533	18.4%
1	3087	37.0%
0	3041	36.4%

f2f_total_03
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	20
Distinct (%)	0.2%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	2.171760758

Minimum	0
Maximum	21
Zeros	2152
Zeros (%)	25.8%
Negative	0
Negative (%)	0.0%
Memory size	65.3 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	2
Q3	3
95-th percentile	6
Maximum	21
Range	21
Interquartile range (IQR)	3

Descriptive statistics

Standard deviation	2.196498675
Coefficient of variation (CV)	1.011390719
Kurtosis	5.622076836
Mean	2.171760758
Median Absolute Deviation (MAD)	1
Skewness	1.73924521
Sum	18119
Variance	4.824606429
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=20)

Value	Count	Frequency (%)
0	2152	25.8%
1	1675	20.1%
2	1551	18.6%
3	1128	13.5%
4	771	9.2%
5	475	5.7%
6	242	2.9%
7	144	1.7%
8	81	1.0%
9	36	0.4%
Other values (10)	88	1.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	2152	25.8%
1	1675	20.1%
2	1551	18.6%
3	1128	13.5%
4	771	9.2%
5	475	5.7%
6	242	2.9%
7	144	1.7%
8	81	1.0%
9	36	0.4%

Value	Count	Frequency (%)
21	1	< 0.1%
18	5	0.1%
17	2	< 0.1%
16	2	< 0.1%
15	4	< 0.1%
14	7	0.1%
13	7	0.1%
12	10	0.1%
11	21	0.3%
10	29	0.3%

f2f_total_06
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	20
Distinct (%)	0.2%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	2.221503056

Minimum	0
Maximum	19
Zeros	2147
Zeros (%)	25.7%
Negative	0
Negative (%)	0.0%
Memory size	65.3 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	2
Q3	3
95-th percentile	6
Maximum	19
Range	19
Interquartile range (IQR)	3

Descriptive statistics

Standard deviation	2.278464489
Coefficient of variation (CV)	1.025640942
Kurtosis	4.834519456
Mean	2.221503056
Median Absolute Deviation (MAD)	1
Skewness	1.717823703
Sum	18534
Variance	5.191400426
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=20)

Value	Count	Frequency (%)
0	2147	25.7%
1	1731	20.7%
2	1379	16.5%
3	1194	14.3%
4	771	9.2%
5	496	5.9%
6	232	2.8%
7	138	1.7%
8	88	1.1%
9	57	0.7%
Other values (10)	110	1.3%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	2147	25.7%
1	1731	20.7%
2	1379	16.5%
3	1194	14.3%
4	771	9.2%
5	496	5.9%
6	232	2.8%
7	138	1.7%
8	88	1.1%
9	57	0.7%

Value	Count	Frequency (%)
19	1	< 0.1%
18	1	< 0.1%
17	1	< 0.1%
16	6	0.1%
15	8	0.1%
14	4	< 0.1%
13	14	0.2%
12	21	0.3%
11	24	0.3%
10	30	0.4%

f2f_total_09
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	18
Distinct (%)	0.2%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	1.924247872

Minimum	0
Maximum	17
Zeros	2486
Zeros (%)	29.8%
Negative	0
Negative (%)	0.0%
Memory size	65.3 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	1
Q3	3
95-th percentile	6
Maximum	17
Range	17
Interquartile range (IQR)	3

Descriptive statistics

Standard deviation	2.021999737
Coefficient of variation (CV)	1.050800038
Kurtosis	4.635370616
Mean	1.924247872
Median Absolute Deviation (MAD)	1
Skewness	1.638157258
Sum	16054
Variance	4.088482936
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=18)

Value	Count	Frequency (%)
0	2486	29.8%
1	1750	21.0%
2	1486	17.8%
3	1081	13.0%
4	702	8.4%
5	386	4.6%
6	202	2.4%
7	106	1.3%
8	57	0.7%
9	34	0.4%
Other values (8)	53	0.6%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	2486	29.8%
1	1750	21.0%
2	1486	17.8%
3	1081	13.0%
4	702	8.4%
5	386	4.6%
6	202	2.4%
7	106	1.3%
8	57	0.7%
9	34	0.4%

Value	Count	Frequency (%)
17	2	< 0.1%
16	2	< 0.1%
15	3	< 0.1%
14	4	< 0.1%
13	10	0.1%
12	4	< 0.1%
11	10	0.1%
10	18	0.2%
9	34	0.4%
8	57	0.7%

f2f_total_12
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct	1
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	65.3 KiB

0	8343

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	8343
Distinct characters	1
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	8343	100.0%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	8343	100.0%

Most occurring characters

Value	Count	Frequency (%)
0	8343	100.0%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	8343	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	8343	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Common	8343	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
0	8343	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	8343	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	8343	100.0%

webinar_total_03
Real number (ℝ_≥0)

HIGH CORRELATION
ZEROS

Distinct	7
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	0.1147069399

Minimum	0
Maximum	6
Zeros	7586
Zeros (%)	90.9%
Negative	0
Negative (%)	0.0%
Memory size	65.3 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	0
Q3	0
95-th percentile	1
Maximum	6
Range	6
Interquartile range (IQR)	0

Descriptive statistics

Standard deviation	0.417092472
Coefficient of variation (CV)	3.636157256
Kurtosis	41.55089498
Mean	0.1147069399
Median Absolute Deviation (MAD)	0
Skewness	5.306139986
Sum	957
Variance	0.1739661302
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=7)

Value	Count	Frequency (%)
0	7586	90.9%
1	621	7.4%
2	95	1.1%
3	29	0.3%
4	5	0.1%
6	4	< 0.1%
5	3	< 0.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	7586	90.9%
1	621	7.4%
2	95	1.1%
3	29	0.3%
4	5	0.1%
5	3	< 0.1%
6	4	< 0.1%

Value	Count	Frequency (%)
6	4	< 0.1%
5	3	< 0.1%
4	5	0.1%
3	29	0.3%
2	95	1.1%
1	621	7.4%
0	7586	90.9%

webinar_total_06
Real number (ℝ_≥0)

HIGH CORRELATION
ZEROS

Distinct	9
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	0.1323265013

Minimum	0
Maximum	9
Zeros	7596
Zeros (%)	91.0%
Negative	0
Negative (%)	0.0%
Memory size	65.3 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	0
Q3	0
95-th percentile	1
Maximum	9
Range	9
Interquartile range (IQR)	0

Descriptive statistics

Standard deviation	0.5074264521
Coefficient of variation (CV)	3.834654791
Kurtosis	51.15379817
Mean	0.1323265013
Median Absolute Deviation (MAD)	0
Skewness	5.889979497
Sum	1104
Variance	0.2574816042
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=9)

Value	Count	Frequency (%)
0	7596	91.0%
1	522	6.3%
2	148	1.8%
3	47	0.6%
4	20	0.2%
7	5	0.1%
5	3	< 0.1%
9	1	< 0.1%
6	1	< 0.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	7596	91.0%
1	522	6.3%
2	148	1.8%
3	47	0.6%
4	20	0.2%
5	3	< 0.1%
6	1	< 0.1%
7	5	0.1%
9	1	< 0.1%

Value	Count	Frequency (%)
9	1	< 0.1%
7	5	0.1%
6	1	< 0.1%
5	3	< 0.1%
4	20	0.2%
3	47	0.6%
2	148	1.8%
1	522	6.3%
0	7596	91.0%

webinar_total_09
Categorical

HIGH CORRELATION

Distinct	4
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	65.3 KiB

0	7956
1	354
2	32
3	1

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	8343
Distinct characters	4
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1 ?
Unique (%)	< 0.1%

Sample

1st row	1
2nd row	0
3rd row	1
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	7956	95.4%
1	354	4.2%
2	32	0.4%
3	1	< 0.1%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	7956	95.4%
1	354	4.2%
2	32	0.4%
3	1	< 0.1%

Most occurring characters

Value	Count	Frequency (%)
0	7956	95.4%
1	354	4.2%
2	32	0.4%
3	1	< 0.1%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	8343	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	7956	95.4%
1	354	4.2%
2	32	0.4%
3	1	< 0.1%

Most occurring scripts

Value	Count	Frequency (%)
Common	8343	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
0	7956	95.4%
1	354	4.2%
2	32	0.4%
3	1	< 0.1%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	8343	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	7956	95.4%
1	354	4.2%
2	32	0.4%
3	1	< 0.1%

webinar_total_12
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct	1
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	65.3 KiB

0	8343

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	8343
Distinct characters	1
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	8343	100.0%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	8343	100.0%

Most occurring characters

Value	Count	Frequency (%)
0	8343	100.0%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	8343	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	8343	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Common	8343	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
0	8343	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	8343	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	8343	100.0%

is_cardiologist
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	65.3 KiB

0	7340
1	1003

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	8343
Distinct characters	2
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	7340	88.0%
1	1003	12.0%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	7340	88.0%
1	1003	12.0%

Most occurring characters

Value	Count	Frequency (%)
0	7340	88.0%
1	1003	12.0%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	8343	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	7340	88.0%
1	1003	12.0%

Most occurring scripts

Value	Count	Frequency (%)
Common	8343	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
0	7340	88.0%
1	1003	12.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	8343	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	7340	88.0%
1	1003	12.0%

is_gp
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	65.3 KiB

1	7340
0	1003

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	8343
Distinct characters	2
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	1
2nd row	1
3rd row	1
4th row	1
5th row	1

Common Values

Value	Count	Frequency (%)
1	7340	88.0%
0	1003	12.0%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
1	7340	88.0%
0	1003	12.0%

Most occurring characters

Value	Count	Frequency (%)
1	7340	88.0%
0	1003	12.0%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	8343	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
1	7340	88.0%
0	1003	12.0%

Most occurring scripts

Value	Count	Frequency (%)
Common	8343	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
1	7340	88.0%
0	1003	12.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	8343	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
1	7340	88.0%
0	1003	12.0%

years_since_graduation
Real number (ℝ_≥0)

Distinct	60
Distinct (%)	0.7%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	27.08845739

Minimum	3
Maximum	68
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	65.3 KiB

Quantile statistics

Minimum	3
5-th percentile	8
Q1	17
median	28
Q3	37
95-th percentile	46
Maximum	68
Range	65
Interquartile range (IQR)	20

Descriptive statistics

Standard deviation	12.37045419
Coefficient of variation (CV)	0.4566688319
Kurtosis	-0.9050269057
Mean	27.08845739
Median Absolute Deviation (MAD)	10
Skewness	0.00792055519
Sum	225999
Variance	153.028137
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
32	247	3.0%
31	246	2.9%
33	245	2.9%
35	244	2.9%
36	238	2.9%
30	236	2.8%
38	228	2.7%
26	221	2.6%
39	220	2.6%
11	218	2.6%
Other values (50)	6000	71.9%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
3	1	< 0.1%
4	42	0.5%
5	86	1.0%
6	134	1.6%
7	151	1.8%
8	199	2.4%
9	202	2.4%
10	185	2.2%
11	218	2.6%
12	177	2.1%

Value	Count	Frequency (%)
68	1	< 0.1%
63	2	< 0.1%
60	2	< 0.1%
59	5	0.1%
58	5	0.1%
57	4	< 0.1%
56	10	0.1%
55	16	0.2%
54	20	0.2%
53	24	0.3%

gender_female
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	65.3 KiB

1.0	5468
0.0	2875

Length

Max length	3
Median length	3
Mean length	3
Min length	3

Characters and Unicode

Total characters	25029
Distinct characters	3
Distinct categories	2 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	1.0
2nd row	0.0
3rd row	0.0
4th row	1.0
5th row	0.0

Common Values

Value	Count	Frequency (%)
1.0	5468	65.5%
0.0	2875	34.5%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
1.0	5468	65.5%
0.0	2875	34.5%

Most occurring characters

Value	Count	Frequency (%)
0	11218	44.8%
.	8343	33.3%
1	5468	21.8%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	16686	66.7%
Other Punctuation	8343	33.3%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	11218	67.2%
1	5468	32.8%

Other Punctuation

Value	Count	Frequency (%)
.	8343	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Common	25029	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
0	11218	44.8%
.	8343	33.3%
1	5468	21.8%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	25029	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	11218	44.8%
.	8343	33.3%
1	5468	21.8%

gender_male
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	65.3 KiB

0.0	5468
1.0	2875

Length

Max length	3
Median length	3
Mean length	3
Min length	3

Characters and Unicode

Total characters	25029
Distinct characters	3
Distinct categories	2 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0.0
2nd row	1.0
3rd row	1.0
4th row	0.0
5th row	1.0

Common Values

Value	Count	Frequency (%)
0.0	5468	65.5%
1.0	2875	34.5%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0.0	5468	65.5%
1.0	2875	34.5%

Most occurring characters

Value	Count	Frequency (%)
0	13811	55.2%
.	8343	33.3%
1	2875	11.5%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	16686	66.7%
Other Punctuation	8343	33.3%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	13811	82.8%
1	2875	17.2%

Other Punctuation

Value	Count	Frequency (%)
.	8343	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Common	25029	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
0	13811	55.2%
.	8343	33.3%
1	2875	11.5%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	25029	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	13811	55.2%
.	8343	33.3%
1	2875	11.5%

hospital
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	65.3 KiB

0.0	7415
1.0	928

Length

Max length	3
Median length	3
Mean length	3
Min length	3

Characters and Unicode

Total characters	25029
Distinct characters	3
Distinct categories	2 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0.0
2nd row	0.0
3rd row	1.0
4th row	1.0
5th row	0.0

Common Values

Value	Count	Frequency (%)
0.0	7415	88.9%
1.0	928	11.1%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0.0	7415	88.9%
1.0	928	11.1%

Most occurring characters

Value	Count	Frequency (%)
0	15758	63.0%
.	8343	33.3%
1	928	3.7%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	16686	66.7%
Other Punctuation	8343	33.3%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	15758	94.4%
1	928	5.6%

Other Punctuation

Value	Count	Frequency (%)
.	8343	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Common	25029	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
0	15758	63.0%
.	8343	33.3%
1	928	3.7%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	25029	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	15758	63.0%
.	8343	33.3%
1	928	3.7%

office
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	65.3 KiB

1.0	7415
0.0	928

Length

Max length	3
Median length	3
Mean length	3
Min length	3

Characters and Unicode

Total characters	25029
Distinct characters	3
Distinct categories	2 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	1.0
2nd row	1.0
3rd row	0.0
4th row	0.0
5th row	1.0

Common Values

Value	Count	Frequency (%)
1.0	7415	88.9%
0.0	928	11.1%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
1.0	7415	88.9%
0.0	928	11.1%

Most occurring characters

Value	Count	Frequency (%)
0	9271	37.0%
.	8343	33.3%
1	7415	29.6%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	16686	66.7%
Other Punctuation	8343	33.3%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	9271	55.6%
1	7415	44.4%

Other Punctuation

Value	Count	Frequency (%)
.	8343	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Common	25029	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
0	9271	37.0%
.	8343	33.3%
1	7415	29.6%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	25029	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	9271	37.0%
.	8343	33.3%
1	7415	29.6%

prescription_total
Real number (ℝ_≥0)

ZEROS

Distinct	66
Distinct (%)	0.8%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	4.243078029

Minimum	0
Maximum	131
Zeros	1527
Zeros (%)	18.3%
Negative	0
Negative (%)	0.0%
Memory size	65.3 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	1
median	2
Q3	5
95-th percentile	14
Maximum	131
Range	131
Interquartile range (IQR)	4

Descriptive statistics

Standard deviation	6.438680739
Coefficient of variation (CV)	1.517455181
Kurtosis	56.14739167
Mean	4.243078029
Median Absolute Deviation (MAD)	2
Skewness	5.599981137
Sum	35400
Variance	41.45660966
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
2	1566	18.8%
0	1527	18.3%
1	1201	14.4%
3	907	10.9%
4	770	9.2%
5	444	5.3%
6	412	4.9%
7	291	3.5%
8	216	2.6%
10	157	1.9%
Other values (56)	852	10.2%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	1527	18.3%
1	1201	14.4%
2	1566	18.8%
3	907	10.9%
4	770	9.2%
5	444	5.3%
6	412	4.9%
7	291	3.5%
8	216	2.6%
9	154	1.8%

Value	Count	Frequency (%)
131	1	< 0.1%
100	1	< 0.1%
96	1	< 0.1%
83	1	< 0.1%
82	1	< 0.1%
78	1	< 0.1%
74	1	< 0.1%
73	1	< 0.1%
72	1	< 0.1%
71	1	< 0.1%

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Count
Matrix

A simple visualization of nullity by column.

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

First rows

	rep_id	conference_total_03	conference_total_09	conference_total_12	email_open_total_03	email_open_total_06	email_open_total_09	email_open_total_12	f2f_total_03	f2f_total_06	f2f_total_09	webinar_total_03	webinar_total_06	webinar_total_09	is_cardiologist	is_gp	years_since_graduation	gender_female	gender_male	hospital	office	prescription_total
0	100	0	1	0	1	0	0	1	4	6	9	0	2	1	0	1	23	1.0	0.0	0.0	1.0	3
1	101	0	1	0	0	0	2	1	0	2	0	0	0	0	0	1	27	0.0	1.0	0.0	1.0	2
2	101	0	0	0	0	1	1	0	1	3	4	0	0	1	0	1	29	0.0	1.0	1.0	0.0	83
3	102	0	0	0	0	0	1	0	0	0	0	0	0	0	0	1	22	1.0	0.0	1.0	0.0	2
4	103	0	0	1	0	2	1	2	3	6	7	0	0	0	0	1	27	0.0	1.0	0.0	1.0	4
5	100	0	0	0	1	0	2	2	3	4	2	1	0	0	0	1	44	1.0	0.0	0.0	1.0	5
6	101	0	0	0	1	0	1	0	3	2	2	0	0	0	0	1	35	1.0	0.0	0.0	1.0	4
7	101	1	0	0	2	1	2	0	2	4	0	0	0	0	0	1	42	1.0	0.0	0.0	1.0	2
8	104	1	0	0	0	0	1	2	9	13	10	0	0	0	1	0	45	1.0	0.0	0.0	1.0	8
9	101	0	0	0	0	0	2	2	4	6	6	0	0	0	0	1	19	0.0	1.0	0.0	1.0	0

Last rows

	rep_id	conference_total_03	conference_total_06	conference_total_09	email_open_total_03	email_open_total_06	email_open_total_09	email_open_total_12	f2f_total_03	f2f_total_06	f2f_total_09	webinar_total_06	is_cardiologist	is_gp	years_since_graduation	gender_female	gender_male	hospital	office	prescription_total
8333	116	1	1	0	0	0	0	0	1	2	1	0	0	1	21	0.0	1.0	0.0	1.0	9
8334	207	0	0	0	2	1	2	1	0	1	0	1	0	1	9	0.0	1.0	0.0	1.0	5
8335	158	0	1	0	1	2	0	0	0	1	0	0	1	0	20	1.0	0.0	0.0	1.0	22
8336	184	0	0	0	2	1	1	3	0	0	1	0	0	1	8	1.0	0.0	0.0	1.0	5
8337	152	0	0	0	0	2	0	0	0	1	0	0	0	1	7	1.0	0.0	0.0	1.0	6
8338	160	0	0	0	0	1	2	0	0	1	4	0	1	0	11	1.0	0.0	1.0	0.0	21
8339	123	0	0	0	1	0	0	0	0	1	1	0	0	1	4	1.0	0.0	0.0	1.0	1
8340	116	0	0	1	0	1	0	2	0	0	1	0	0	1	16	1.0	0.0	0.0	1.0	3
8341	152	0	0	0	1	1	1	2	0	0	1	0	0	1	27	1.0	0.0	0.0	1.0	9
8342	115	0	0	0	0	3	0	0	0	0	1	0	0	1	3	1.0	0.0	0.0	1.0	1

	rep_id	conference_total_03	conference_total_09	conference_total_12	email_open_total_03	email_open_total_06	email_open_total_09	email_open_total_12	f2f_total_03	f2f_total_06	f2f_total_09	webinar_total_03	webinar_total_06	webinar_total_09	is_cardiologist	is_gp	years_since_graduation	gender_female	gender_male	hospital	office	prescription_total
0	100	0	1	0	1	0	0	1	4	6	9	0	2	1	0	1	23	1.0	0.0	0.0	1.0	3
1	101	0	1	0	0	0	2	1	0	2	0	0	0	0	0	1	27	0.0	1.0	0.0	1.0	2
2	101	0	0	0	0	1	1	0	1	3	4	0	0	1	0	1	29	0.0	1.0	1.0	0.0	83
3	102	0	0	0	0	0	1	0	0	0	0	0	0	0	0	1	22	1.0	0.0	1.0	0.0	2
4	103	0	0	1	0	2	1	2	3	6	7	0	0	0	0	1	27	0.0	1.0	0.0	1.0	4
5	100	0	0	0	1	0	2	2	3	4	2	1	0	0	0	1	44	1.0	0.0	0.0	1.0	5
6	101	0	0	0	1	0	1	0	3	2	2	0	0	0	0	1	35	1.0	0.0	0.0	1.0	4
7	101	1	0	0	2	1	2	0	2	4	0	0	0	0	0	1	42	1.0	0.0	0.0	1.0	2
8	104	1	0	0	0	0	1	2	9	13	10	0	0	0	1	0	45	1.0	0.0	0.0	1.0	8
9	101	0	0	0	0	0	2	2	4	6	6	0	0	0	0	1	19	0.0	1.0	0.0	1.0	0

	rep_id	conference_total_03	conference_total_09	conference_total_12	email_open_total_03	email_open_total_06	email_open_total_09	email_open_total_12	f2f_total_03	f2f_total_06	f2f_total_09	webinar_total_03	webinar_total_06	webinar_total_09	is_cardiologist	is_gp	years_since_graduation	gender_female	gender_male	hospital	office	prescription_total
0	100	0	1	0	1	0	0	1	4	6	9	0	2	1	0	1	23	1.0	0.0	0.0	1.0	3
1	101	0	1	0	0	0	2	1	0	2	0	0	0	0	0	1	27	0.0	1.0	0.0	1.0	2
2	101	0	0	0	0	1	1	0	1	3	4	0	0	1	0	1	29	0.0	1.0	1.0	0.0	83
3	102	0	0	0	0	0	1	0	0	0	0	0	0	0	0	1	22	1.0	0.0	1.0	0.0	2
4	103	0	0	1	0	2	1	2	3	6	7	0	0	0	0	1	27	0.0	1.0	0.0	1.0	4
5	100	0	0	0	1	0	2	2	3	4	2	1	0	0	0	1	44	1.0	0.0	0.0	1.0	5
6	101	0	0	0	1	0	1	0	3	2	2	0	0	0	0	1	35	1.0	0.0	0.0	1.0	4
7	101	1	0	0	2	1	2	0	2	4	0	0	0	0	0	1	42	1.0	0.0	0.0	1.0	2
8	104	1	0	0	0	0	1	2	9	13	10	0	0	0	1	0	45	1.0	0.0	0.0	1.0	8
9	101	0	0	0	0	0	2	2	4	6	6	0	0	0	0	1	19	0.0	1.0	0.0	1.0	0

Overview

Variables

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Decimal Number

Most occurring scripts

Most frequent character per script

Common

Most occurring blocks

Most frequent character per block

ASCII

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Decimal Number

Most occurring scripts

Most frequent character per script

Common

Most occurring blocks

Most frequent character per block

ASCII

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Decimal Number

Most occurring scripts

Most frequent character per script

Common

Most occurring blocks

Most frequent character per block

ASCII

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Decimal Number

Most occurring scripts

Most frequent character per script

Common

Most occurring blocks

Most frequent character per block

ASCII

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Decimal Number

Most occurring scripts

Most frequent character per script

Common

Most occurring blocks

Most frequent character per block

ASCII

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Decimal Number

Most occurring scripts

Most frequent character per script

Common

Most occurring blocks

Most frequent character per block

ASCII

	rep_id	conference_total_03	conference_total_09	conference_total_12	email_open_total_03	email_open_total_06	email_open_total_09	email_open_total_12	f2f_total_03	f2f_total_06	f2f_total_09	webinar_total_03	webinar_total_06	webinar_total_09	is_cardiologist	is_gp	years_since_graduation	gender_female	gender_male	hospital	office	prescription_total
0	100	0	1	0	1	0	0	1	4	6	9	0	2	1	0	1	23	1.0	0.0	0.0	1.0	3
1	101	0	1	0	0	0	2	1	0	2	0	0	0	0	0	1	27	0.0	1.0	0.0	1.0	2
2	101	0	0	0	0	1	1	0	1	3	4	0	0	1	0	1	29	0.0	1.0	1.0	0.0	83
3	102	0	0	0	0	0	1	0	0	0	0	0	0	0	0	1	22	1.0	0.0	1.0	0.0	2
4	103	0	0	1	0	2	1	2	3	6	7	0	0	0	0	1	27	0.0	1.0	0.0	1.0	4
5	100	0	0	0	1	0	2	2	3	4	2	1	0	0	0	1	44	1.0	0.0	0.0	1.0	5
6	101	0	0	0	1	0	1	0	3	2	2	0	0	0	0	1	35	1.0	0.0	0.0	1.0	4
7	101	1	0	0	2	1	2	0	2	4	0	0	0	0	0	1	42	1.0	0.0	0.0	1.0	2
8	104	1	0	0	0	0	1	2	9	13	10	0	0	0	1	0	45	1.0	0.0	0.0	1.0	8
9	101	0	0	0	0	0	2	2	4	6	6	0	0	0	0	1	19	0.0	1.0	0.0	1.0	0